Human-Inspired Autonomous Racing in Low Friction Environments

Abstract

Low friction surfaces such as ice and snow pose a significant challenge for autonomous vehicle maneuvering due to the limited available traction. While some prior works have successfully controlled a vehicle up to the friction limit, generalized autonomous driving in these conditions remains an open problem. Skilled human drivers, however, display exceptional vehicle control in these challenging environments. In particular, rally drivers operate with large sideslip angles and high tire slip to achieve their objective of minimizing time. We directly compare a professional human driver and state-of-the-art autonomous racing controller to reveal dramatic differences in state- and input- space utilization. Inspired by these ideas, we present a motion planning and control framework for autonomous racing in low friction environments. We first develop a vehicle model to capture the dynamics associated with high slip maneuvering. Then, we implement nonlinear model predictive control for online, time optimal trajectory generation. Experimental validation on a frozen lake testing track using a Volkswagen Golf GTI reveals the ability to safely operate beyond limits of conventional vehicle safety systems. The utility of this increased maneuverability is demonstrated when the controller is able to recover from a significant, unexpected disturbance to the yaw dynamics. These results suggest that autonomous vehicles, like skilled human drivers, can leverage increased slip to improve robustness in low friction environments.